Body-worn monitor for measuring respiration rate

ABSTRACT

The invention provides a multi-sensor system that uses an algorithm based on adaptive filtering to monitor a patient&#39;s respiratory rate. The system features a first sensor which is selected from the group consisting of an impedance pneumography sensor, an ECG sensor, a PPG sensor, and a motion sensor (e.g., an accelerometer) configured to attach to the patient&#39;s torso and measure therefrom a motion signal. The system further comprises (iii) a processing system, configured to operably connect to the first and motion sensors, and to determine a respiration rate value by applying filter parameters obtained from the first sensor signals to the motion sensor signals.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/559,429, filed Sep. 14, 2009, now U.S. Pat. No. 10,123,722, thecontents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to medical devices for monitoring vitalsigns, e.g., respiration rate.

Description of the Related Art

Respiration rate (RR) is a vital sign typically measured in the hospitalusing either an indirect electrode-based technique called ‘impedancepneumography’ (IP), a direct optical technique called ‘end-tidal CO2’(et-CO2), or simply through manual counting of breaths by a medicalprofessional. IP is typically used in lower-acuity areas of thehospital, and uses the same electrodes deployed in a conventional‘Einthoven's triangle’ configuration for measuring heart rate (HR) froman electrocardiogram (ECG). One of the electrodes supplies alow-amperage (˜4 mA) current that is typically modulated at a highfrequency (˜50-100 kHz). Current passes through a patient's chestcavity, which is characterized by a time-dependent capacitance thatvaries with each breath. A second electrode detects the current, whichis modulated by the changing capacitance. Ultimately this yields ananalog signal that is processed with a series of amplifiers and filtersto detect the time-dependent capacitance change and, subsequently, thepatient's RR.

In et-CO2, a device called a capnometer features a small plastic tubethat typically inserts in the patient's mouth. With each breath the tubecollects expelled CO2. A beam of infrared radiation emitted from anintegrated light source passes through the CO2 and is absorbed in atime-dependent manner that varies with the breathing rate. Aphotodetector and series of processing electronics analyze thetransmitted signal to determine RR. et-CO2 systems are typically used inhigh-acuity areas of the hospital, such as the intensive care unit(ICU), where patients often use ventilators to assist them in breathing.

In yet another technique, RR is measured from the envelope of atime-dependent optical waveform called a photoplethysmogram (PPG) thatis measured from the index finger during a conventional measurement ofthe patient's oxygen saturation (SpO2). Breathing changes the oxygencontent in the patient's blood and, subsequently, its optical absorptionproperties. Such changes cause a slight, low-frequency variation in thePPG that can be detected with a pulse oximeter's optical system, whichtypically operates at both red and infrared wavelengths.

Not surprisingly, RR is an important predictor of a decompensatingpatient. For example, a study in 1993 concluded that a respiratory rategreater than 27 breaths/minute was the most important predictor ofcardiac arrests in hospital wards (Fieselmann et al., ‘Respiratory ratepredicts cardiopulmonary arrest for internal medicine patients’, J GenIntern Med 1993; 8: 354-360). Subbe et al. found that, in unstablepatients, relative changes in respiratory rate were much greater thanchanges in heart rate or systolic blood pressure, and thus that therespiratory rate was likely to be a better means of discriminatingbetween stable patients and patients at risk (Subbe et al., ‘Effect ofintroducing the Modified Early Warning score on clinical outcomes,cardiopulmonary arrests and intensive care utilization in acute medicaladmissions’, Anaesthesia 2003; 58: 797-802). Goldhill et al. reportedthat 21% of ward patients with a respiratory rate of 25-29breaths/minute assessed by a critical care outreach service died inhospital (Goldhill et al., ‘A physiologically-based early warning scorefor ward patients: the association between score and outcome’,Anaesthesia 2005; 60: 547-553). Those with a higher respiratory rate hadan even higher mortality rate. In another study, just over half of allpatients suffering a serious adverse event on the general wards (e.g. acardiac arrest or ICU admission) had a respiratory rate greater than 24breaths/minute. These patients could have been identified as high riskup to 24 hours before the event with a specificity of over 95% (Cretikoset al., ‘The Objective Medical Emergency Team Activation Criteria: acase-control study’, Resuscitation 2007; 73: 62-72). Medical referencessuch as these clearly indicate that an accurate, easy-to-use device formeasuring respiratory rate is important for patient monitoring withinthe hospital.

Despite its importance and the large number of available monitoringtechniques, RR is notoriously difficult to measure, particularly when apatient is moving. During periods of motion, non-invasive techniquesbased on IP and PPG signals are usually overwhelmed by artifacts andthus completely ineffective. This makes it difficult or impossible tomeasure RR from an ambulatory patient. Measurements based on et-CO2 aretypically less susceptible to motion, but require a plastic tubeinserted in the patient's mouth, which is typically impractical forambulatory patients.

SUMMARY OF THE INVENTION

This invention provides methods, devices, and systems for use inmeasuring RR using multiple input signals, including IP, PPG, and ECGwaveforms, and a signal processing technique based on adaptivefiltering. After being measured with a body-worn system, these waveformsare processed along with those from an accelerometer mounted on thepatient's torso (most typically the chest or abdomen). The accelerometermeasures small, breathing-induced movements to generate a time-dependentwaveform (ACC). With adaptive filtering, an initial RR is preferablyestimated from the IP waveform, and alternatively from the PPG or ECGwaveform. The initial RR is then processed and used to determineparameters for a bandpass digital filter, typically implemented with afinite impulse response function. This yields a customized filteringfunction which then processes the ACC waveform. The filtering functiongenerates a relatively noise-free ACC waveform with well-defined pulsescorresponding to RR. Each pulse can then be further processed andcounted to determine an accurate RR value, even during periods ofmotion.

The body-worn monitor measures IP, PPG, ECG, and ACC waveforms with aseries of sensors integrated into a comfortable, low-profile system thatpreferably communicates wirelessly with a remote computer in thehospital. The system typically features three accelerometers, eachconfigured to measure a unique signal along its x, y, and z axes, toyield a total of nine ACC waveforms. In certain embodiments, theaccelerometers are deployed on the patient's torso, upper arm, and lowerarm, and may be embedded in the monitor's cabling or processing unit.Each ACC waveform can be additionally processed to determine thepatient's posture, degree of motion, and activity level. Theseparameters serve as valuable information that can ultimately reduceoccurrences of ‘false positive’ alarms/alerts in the hospital. Forexample, if processing of additional ACC waveforms indicates a patientis walking, then their RR rate, which may be affected by walking-inducedartifacts, can be ignored by an alarm/alert engine associated with thebody-worn monitor. The assumption in this case is that a walking patientis likely relatively healthy, regardless of their RR value. Perhaps moreimportantly, with a conventional monitoring device a walking patient mayyield a noisy IP signal that is then processed to determine anartificially high RR, which then triggers a false alarm. Such asituation can be avoided with an independent measurement of motion, suchas that described herein. Other heuristic rules based on analysis of ACCwaveforms may also be deployed according to this invention.

Sensors attached to the wrist and bicep each measure signals that arecollectively analyzed to estimate the patient's arm height; this can beused to improve accuracy of a continuous blood pressure measurement(cNIBP), as described below, that measures systolic (SYS), diastolic(DIA), and mean (MAP) arterial blood pressures. And the sensor attachedto the patient's chest measures signals that are analyzed to determineposture and activity level, which can affect measurements for RR, SpO2,cNIBP, and other vital signs. Algorithms for processing information fromthe accelerometers for these purposes are described in detail in thefollowing patent applications, the contents of which are fullyincorporated herein by reference:BODY-WORN MONITOR FEATURING ALARMSYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No.12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITHSYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094;filed May 20, 2009). As described therein, knowledge of a patient'smotion, activity level, and posture can greatly enhance the accuracy ofalarms/alerts generated by the body-worn monitor.

The body-worn monitor features systems for continuously monitoringpatients in a hospital environment, and as the patient transfers fromdifferent areas in the hospital, and ultimately to the home. Both SpO2and cNIBP rely on accurate measurement of PPG and ACC waveforms, alongwith an ECG, from patients that are both moving and at rest. cNIBP istypically measured with the ‘Composite Technique’, which is described indetail in the co-pending patent application entitled: VITAL SIGN MONITORFOR MEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSUREWAVEFORMS (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contentsof which are fully incorporated herein by reference.

As described in these applications, the Composite Technique (or,alternatively, the ‘Hybrid Technique’ referred to therein) typicallyuses a single PPG waveform from the SpO2 measurement (typicallygenerated with infrared radiation), along with the ECG waveform, tocalculate a parameter called ‘pulse transit time’ (PTT) which stronglycorrelates to blood pressure. Specifically, the ECG waveform features asharply peaked QRS complex that indicates depolarization of the heart'sleft ventricle, and, informally, provides a time-dependent marker of aheart beat. PTT is the time separating the peak of the QRS complex andthe onset, or ‘foot’, of the PPG waveforms. The QRS complex, along withthe foot of each pulse in the PPG, can be used to more accuratelyextract AC signals using a mathematical technique described in detailbelow. In other embodiments both the red and infrared PPG waveforms arecollectively processed to enhance the accuracy of the cNIBP measurement.

In certain embodiments, the electrical system for measuring RR featuresa small-scale, low-power circuit mounted on a circuit board that fitswithin the wrist-worn transceiver. The transceiver additionally includesa touchpanel display, barcode reader, and wireless systems for ancillaryapplications described, for example, in the above-referencedapplications, the contents of which have been previously incorporatedherein by reference.

In one aspect, the invention provides a multi-sensor system that uses analgorithm based on adaptive filtering to monitor a patient's RR. Thesystem features a first sensor selected from the following group: i) anIP sensor featuring at least two electrodes and an IP processing circuitconfigured to measure an IP signal; ii) an ECG sensor featuring at leasttwo electrodes and an ECG processing circuit configured to measure anECG signal; and iii) a PPG sensor featuring a light source,photodetector, and PPG processing circuit configured to measure a PPGsignal. Each of these sensors measures a time-dependent signal which issensitive to RR and is processed to determine an initial RR value. Thesystem features a second sensor (e.g. a digital 3-axis accelerometer)that attaches to the patient's torso and measures an ACC signalindicating movement of the chest or abdomen that is also sensitive toRR.

A body-worn processing system receives a first signal representing atleast one of the IP, ECG, and PPG signals, and a second signalrepresenting the ACC signal. The processing system is configured to: i)process the first signal to determine an initial RR; ii) process thesecond signal with a digital filter determined from the initial RR todetermine a third signal; and iii) process the third signal to determinea final value for the patient's RR.

The processing system, as described herein, can include one or moremicroprocessors. For example, it can include first microprocessorembedded within a single ASIC that also measures IP and ECG, or mountedon a circuit board that also contains the ASIC or an equivalent circuitmade from discrete components. In these cases the first microprocessoris mounted on the patient's torso. A wrist-worn transceiver can containthe second microprocessor. In embodiments, the first microprocessormounted on the patient's torso determines a RR from multipletime-dependent signals; this value is transmitted to the secondmicroprocessor within the wrist-worn transceiver as a digital or analogdata stream transmitted through a cable. The second microprocessorfurther processes the RR value alongside data describing the patient'smotion and other vital signs. The secondary processing, for example, canbe used to generate alarms/alerts based on RR, or suppress alarms/alertsbecause of the patient's motion.

In embodiments, the digital filter used for adaptive filtering is abandpass filter or low-pass filter. Typically the digital filter isdetermined from a finite impulse response function. The bandpass filtertypically features an upper frequency limit determined from a multiple(e.g. 1-3×) of the initial RR. Such a digital filter is used to processtime-dependent waveforms to remove noise and other artifacts todetermine the initial version of RR. In this case the filter is notadaptive, and instead has a pre-determined passband. The final versionof RR is determined from the adaptive filter, which as described abovehas a passband that depends on the initial version of RR.

In other embodiments, the processing system is further configured todetermine both initial and final versions of RR by processing a filteredwaveform with a mathematical derivative and then determine a zero-pointcrossing indicating a ‘count’ marking a respiratory event. Such countsare evident in the processed IP signal, which features a first series ofpulses that, once analyzed by the processing system, yields the initialRR. Alternatively, the initial RR is determined from either an ECG orPPG, both of which feature a series of heartbeat-induced pulses withamplitudes characterized by a time-varying envelope, with the frequencyof the envelope representing the initial RR. The waveforms used todetermine the initial and final values for RR can be interchanged, e.g.the ACC waveform can be processed to determine the initial RR value, andthis can then be used to design a digital filter that processes the IP,ECG, or PPG waveforms to determine the final RR value. In general,according to the invention, any combination of the above-describedwaveforms can be used in the adaptive filtering process to determine theinitial and final RR values.

In another aspect, the invention provides a system for monitoring apatient's RR that also accounts for their posture, activity level, anddegree of motion. Such patient states can result in artifacts thataffect the RR measurement, and thus proper interpretation of them canreduce the occurrence of erroneous RR values and ultimately falsealarms/alerts in the hospital.

In another aspect, the invention provides a cable within a body-wornmonitor that includes an IP system, a motion sensor (e.g.accelerometer), and a processing system that determines RR from signalsgenerated by these sensors. These components, for example, can beincluded in a terminal end of the cable, typically worn on the patient'storso, which connects to a series of disposable electrodes that attachto the patient's body. A mechanical housing, typically made of plastic,covers these and other components, such as sensors for measuring signalsrelating to ECG and skin temperature.

In embodiments, the cable includes at least one conductor configured totransmit both a first digital data stream representing the digital IPsignal or information calculated therefrom, and a second digital datastream representing the digital motion signal or information calculatedtherefrom. In other embodiments these signals are processed by amicroprocessor on the chest to determine an RR value, and this value isthen sent in the digital data stream to another processor, such as onewithin the wrist-worn transceiver, where it is further processed. Totransmit the serial data stream, the terminal portion of the cable caninclude a transceiver component, e.g. a serial transceiver configured totransmit a digital data stream according to the CAN protocol. Otherproperties, such as heart rate, temperature, alarms relating to ECGsignals, and other information relating to the CAN communicationprotocol and its timing can be transmitted by the transceiver component.

In embodiments, both the IP and ECG systems are contained within asingle integrated circuit. The ECG system can be modular and determinemulti-lead ECG signals, such as three, five, and twelve-lead ECGsignals.

In another aspect, the invention provides a method for determining RRduring periods of motion. The method includes the following steps: (a)measuring a first time-dependent signal by detecting a modulatedelectrical current passing through the patient's torso; (b) measuring asecond time-dependent signal by detecting respiration-induced movementsin the patient's torso with at least one motion sensor; (c) determininga motion-related event not related to the patient's respiration ratevalue by processing signals from the motion sensor; and (d) collectivelyprocessing both the first and second time-dependent signals to determinea value for RR corresponding to a period when the patient'smotion-related event is below a pre-determined threshold. For example,the motion-related event determined during step (c) can be the patient'sposture, activity level, or degree of motion. Typically these parametersare determined from signals measured with an accelerometer mounted onthe patient's torso. These signals are processed with an algorithm,described in detail below, that yields a vector indicating orientationof the patient's chest and their subsequent posture. Specifically, anangle separating the vector from a pre-determined coordinate systemultimately yields posture, as is described in detail below. Activitylevel (corresponding, e.g., to moving, walking, falling, convulsing) canbe calculated from a mathematical transform of time-dependent variationsof a motion signal that yields a frequency-domain spectrum. Portions ofthe spectrum (e.g. the power of specific frequency components) arecompared to pre-determined frequency parameters to determine theactivity level. Other operations, such as a mathematical derivative ofthe time-dependent motion signal, or a series of ‘decision rules’ basedon a decision-tree algorithm, can also yield the activity level.

In another aspect, the invention provides a method for suppressingalarms related to RR by processing the patient's posture, activitylevel, and degree of motion as determined by the accelerometer. Forexample, the alarm can be suppressed if the patient is standing upright,or if their posture changes from lying down to one of sitting andstanding upright. Or the alarm can be suppressed if their posturechanges from either standing upright or sitting to lying down. Ingeneral, a rapid change in posture, which can be determined with thechest-worn accelerometer, may disrupt the signals used to determine RRto the point where a false alarm/alert is generated. In this embodiment,posture is determined from the vector-based analysis, described above.

In yet another aspect, the invention provides a system for monitoring apatient's RR featuring a sensor unit configured to be mounted on thepatient's torso. The sensor unit features IP and motion sensors, asdescribed above, and additionally attaches directly to an electrode thatsecures the unit to the patient's torso (e.g. chest or abdomen). Here, ahousing comprising the IP and motion sensors additionally includes aconnector featuring an opening configured to receive a metal snap on theexterior of a conventional disposable electrode. Other electrodes usedfor IP and ECG measurements connect to the unit through cables. The unitcan additionally send a digital data stream including RR data over a CANbus to a wrist-worn transceiver, which as described above can furtherprocess the RR value to account for alarms/alerts, motion, etc.

In all embodiments, the wrist-worn transceiver can include a displayconfigured to display the patient's RR and other vital signs, along witha touchpanel interface. A wireless transceiver within the wrist-worntransceiver can transmit information to a remote computer usingconventional protocols such as 802.11, 802.15.4, and cellular. Theremote computer, for example, can be connected to a hospital network. Itcan also be a portable computer, such as a tablet computer, personaldigital assistant, or cellular phone.

Many advantages are associated with this invention. In general, itprovides an accurate measurement of RR, along with an independentmeasurement of a patient's posture, activity level, and motion, tocharacterize an ambulatory patient in the hospital. These parameters canbe collectively analyzed to improve true positive alarms while reducingthe occurrence of false positive alarms. Additionally, the measurementof RR is performed with a body-worn monitor that is comfortable,lightweight, and low-profile, making it particularly well suited forpatients that are moving about. Such a monitor could continuouslymonitor a patient as, for example, they transition from the emergencydepartment to the ICU, and ultimately to the home after hospitalization.

Still other embodiments are found in the following detailed descriptionof the invention and in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a schematic view of a patient wearing accelerometers ontheir abdomen (position 1) and chest (position 2) to measure ACCwaveforms and RR according to the adaptive filtering process of theinvention; FIG. 1B shows a schematic view of the accelerometers fromFIG. 1 along with their three-dimensional measurement axes;

FIG. 2A shows a schematic view of a patient wearing ECG electrodes ontheir chest in a conventional Einthoven's triangle configuration tomeasure an IP waveform; FIG. 2B shows a schematic view of ECG and IPcircuits that simultaneously process signals from each ECG electrode inFIG. 2A to determine both ECG and IP waveforms;

FIGS. 3A-D each show an ACC waveform measured with the configurationshown in FIG. 1 after processing with no filter (FIG. 3A; top), a 0.01→1Hz bandpass filter (FIG. 3B), a 0.01→0.5 Hz bandpass filter (FIG. 3C),and a 0.01→0.1 Hz bandpass filter (FIG. 3C; bottom); FIGS. 3E-H show,respectively, time-dependent derivatives of the ACC waveforms shown inFIGS. 3A-D;

FIGS. 4A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 4A; top), an IP waveform (FIG. 4B), and a et-CO2 waveform(FIG. 4C; bottom) simultaneously measured from a supine patientundergoing slow, deep breaths;

FIGS. 5A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 5A; top), an IP waveform (FIG. 5B), and a et-CO2 waveform(FIG. 5C; bottom) simultaneously measured from a supine patientundergoing fast, deep breaths;

FIGS. 6A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 6A; top), an IP waveform (FIG. 6B), and a et-CO2 waveform(FIG. 6C; bottom) simultaneously measured from a supine patientundergoing very fast, deep breaths;

FIGS. 7A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 7A; top), an IP waveform (FIG. 7B), and a et-CO2 waveform(FIG. 7C; bottom) simultaneously measured from a supine patientundergoing medium, shallow breaths;

FIGS. 8A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 8A; top), an IP waveform (FIG. 8B), and a et-CO2 waveform(FIG. 8C; bottom) simultaneously measured from a standing patientundergoing medium, shallow breaths;

FIGS. 9A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 9A; top), an IP waveform (FIG. 9B), and a et-CO2 waveform(FIG. 9C; bottom) simultaneously measured from a standing patientundergoing fast, deep breaths;

FIGS. 10A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 10A; top), an IP waveform (FIG. 10B), and a et-CO2 waveform(FIG. 10C; bottom) simultaneously measured from a supine patientundergoing slow, deep breaths, followed by a period of apnea, followedby relatively fast, deep breaths;

FIGS. 11A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 11A; top), an IP waveform (FIG. 11B), and a et-CO2 waveform(FIG. 11C; bottom) simultaneously measured from a supine patientundergoing very fast, shallow breaths, followed by a period of apnea,followed by relatively slow, shallow breaths;

FIGS. 12A-C show an ACC waveform filtered with a 0.01→0.1 Hz bandpassfilter (FIG. 12A; top), an IP waveform (FIG. 12B), and a et-CO2 waveform(FIG. 12C; bottom) simultaneously measured from a walking patientundergoing fast, deep breaths;

FIG. 13 shows a flow chart along with ACC and IP waveforms used todetermine RR using an adaptive filtering technique;

FIG. 14 shows a flow chart that describes details of the adaptivefiltering technique shown in FIG. 13;

FIGS. 15A-E show graphs of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter (FIG. 15A; top), an IP waveform filteredinitially with a 0.01→12 Hz bandpass (FIG. 15B), an ACC waveformadaptively filtered with a bandpass filter ranging from 0.01 Hz to 1.5times the breathing rate calculated from the IP waveform in FIG. 15B(FIG. 15C), a first derivative of the filtered waveform in FIG. 15C(FIG. 15D), and the adaptively filtered waveform in FIG. 15C along withmarkers (FIG. 15E; bottom) indicating slow, deep breaths as determinedfrom the algorithm shown by the flow chart in FIG. 14; FIG. 15F is aflow chart showing the algorithmic steps used to process the waveformsshown in FIGS. 15A-E;

FIGS. 16A-E show graphs of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter (FIG. 16A; top), an IP waveform filteredinitially with a 0.01→12 Hz bandpass (FIG. 16B), an ACC waveformadaptively filtered with a bandpass filter ranging from 0.01 Hz to 1.5times the breathing rate calculated from the IP waveform in FIG. 16B(FIG. 16C), a first derivative of the filtered waveform in FIG. 16C(FIG. 16D), and the adaptively filtered waveform in FIG. 16C along withmarkers (FIG. 16E; bottom) indicating fast, deep breaths as determinedfrom the algorithm shown by the flow chart in FIG. 14; FIG. 16F is aflow chart showing the algorithmic steps used to process the waveformsshown in FIGS. 16A-E;

FIGS. 17A-E show graphs of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter (FIG. 17A; top), an IP waveform filteredinitially with a 0.01→12 Hz bandpass (FIG. 17B), an ACC waveformadaptively filtered with a bandpass filter ranging from 0.01 Hz to 1.5times the breathing rate calculated from the IP waveform in FIG. 17B(FIG. 17C), a first derivative of the filtered waveform in FIG. 17C(FIG. 17D), and the adaptively filtered waveform in FIG. 17C along withmarkers (FIG. 17E; bottom) indicating very fast, deep breaths asdetermined from the algorithm shown by the flow chart in FIG. 14; FIG.17F is a flow chart showing the algorithmic steps used to process thewaveforms shown in FIGS. 17A-E;

FIGS. 18A-B show graphs of an ACC waveform filtered initially with a0.01→2 Hz bandpass filter (FIG. 18A; top), and an IP waveform filteredinitially with a 0.01→12 Hz bandpass (FIG. 18B; bottom) measured from awalking patient; FIG. 18C is a flow chart showing the algorithmic stepsused to process the waveforms shown in FIGS. 18A-B;

FIG. 19 is a graph showing correlation between respiratory ratesmeasured with the adaptive filtering technique shown by the flow chartin FIG. 14 and et-CO2;

FIG. 20A is a graph showing an unfiltered ECG waveform measured from aresting patient; FIG. 20B is a graph showing the time-dependent envelopeof the ECG waveform shown in FIG. 20A; FIG. 20C is a graph showing anunfiltered PPG waveform measured simultaneously with the ECG waveform ofFIG. 20A; FIG. 20D is a graph showing the time-dependent envelope of thePPG waveform shown in FIG. 20C; FIG. 20E is a graph showing an IPwaveform measured simultaneously with the ECG waveform of FIG. 20A andthe PPG waveform of FIG. 20C;

FIGS. 21A-C show graphs of time-dependent ECG waveforms (FIG. 21A; top),PPG waveforms (FIG. 21B), and ACC waveforms (FIG. 21C; bottom) measuredalong the x, y, and z-axes for a resting patient;

FIGS. 22A-C show graphs of time-dependent ECG waveforms (FIG. 22A; top),PPG waveforms (FIG. 22B), and ACC waveforms (FIG. 22C; bottom) measuredalong the x, y, and z-axes for a walking patient;

FIGS. 23A-C show graphs of time-dependent ECG waveforms (FIG. 23A; top),PPG waveforms (FIG. 23B), and ACC waveforms (FIG. 23C; bottom) measuredalong the x, y, and z-axes for a convulsing patient;

FIGS. 24A-C show graphs of time-dependent ECG waveforms (FIG. 24A; top),PPG waveforms (FIG. 24B), and ACC waveforms (FIG. 24C; bottom) measuredalong the x, y, and z-axes for a falling patient;

FIG. 25 shows a schematic view of the patient of FIG. 1 and a coordinateaxis used with an algorithm and ACC waveforms to determine the patient'sposture;

FIG. 26A shows a graph of time-dependent ACC waveforms measured from apatient's chest during different postures; FIG. 26B shows a graph oftime-dependent postures determined by processing the ACC waveforms ofFIG. 26A with an algorithm and coordinate axis shown in FIG. 25;

FIGS. 27A and 27B show, respectively, a three-dimensional image of thebody-worn monitor of the invention attached to a patient during andafter an initial indexing measurement;

FIG. 28 shows a three-dimensional image of the wrist-worn transceiverused with the body-worn monitor of FIGS. 27A and 27B;

FIG. 29A is a schematic view of a patient wearing an alternateembodiment of the invention featuring a sensor unit for measuring IP andACC waveforms that connects directly to the patient's abdomen with anelectrode; and FIG. 29B is a schematic, cross-sectional view of thesensor unit of FIG. 29A connected to the patient's abdomen with anelectrode.

DETAILED DESCRIPTION OF THE INVENTION

Sensor Configuration

Referring to FIGS. 1A and 1B, a pair of accelerometers 12, 14 attach,respectively, to the chest and abdomen of a patient 10 to predict RRthrough the patient's torso movement and an algorithm based on adaptivefiltering. Each accelerometer 12, 14 simultaneously measuresacceleration (e.g. motion) along x, y, and z axes of a local coordinatesystem 18. As shown in FIG. 1B, the accelerometers 12, 14 are preferablyaligned so the z axis points into the patient's torso. Within eachaccelerometer 12, 14 is an internal analog-to-digital converter thatgenerates a digital ACC waveform 19 corresponding to each axis.Waveforms are sent as a stream of digital data to a wrist-worntransceiver (shown, for example, in FIGS. 27A, B, and 28) where they areprocessed using an adaptive filtering algorithm described in detailbelow to determine the patient's RR. Alternatively, the adaptivefiltering algorithm can be performed with a microprocessor mountedproximal to the accelerometers 12, 14 on the patient's torso. Additionalproperties such as the patient's posture, degree of motion, and activitylevel are determined from these same digital ACC waveforms. As indicatedby FIG. 1B, the axis within the accelerometer's coordinate system 18that is aligned along the patient's torso (and thus orthogonal to theirrespiration-induced torso movement) is typically more sensitive toevents not related to respiration, e.g. walking and falling.

In a preferred embodiment, digital accelerometers manufactured by AnalogDevices (e.g. the ADXL345 component) are used in the configuration shownin FIG. 1A. These sensors detect acceleration over a range of +/−2 g(or, alternatively, up to +/−8 g) with a small-scale, low-power circuit.

Many patient's are classified as ‘abdomen breathers’, meaning duringrespiration their abdomen undergoes larger movements than their chest. Arelative minority of patients are ‘chest breathers’, indicating that itis the chest that undergoes the larger movements. For this reason it ispreferred that RR is determined using an ACC waveform detected along thez-axis with an accelerometer 14 positioned on the patient's abdomen. Inalternate configurations the accelerometer 12 on the chest can be usedin its place or two augment data collected with the abdomen-mountedsensor. Typically, ACC waveforms along multiple axes (e.g. the x andy-axes) are also modulated by breathing patterns, and can thus be usedto estimate RR. In still other configurations multiple signals from oneor both accelerometers 12, 14 are collectively processed to determine asingle ‘effective’ ACC waveform representing, e.g., an average of thetwo waveforms. This waveform is then processed using adaptive filteringto determine the patient's RR.

As shown in FIGS. 2A and 2B, ECG waveforms are simultaneously measuredwith the ACC waveforms using a trio of electrodes 20, 22, 24 typicallypositioned on the chest of the patient 10 in an Einthoven's triangleconfiguration. During a measurement, each electrode 20, 22, 24 measuresa unique analog signal that passes through a shielded cable to an ECGcircuit 26, which is typically mounted in a small plastic box 25attached to the patient's chest. The ECG circuit 26 typically includes adifferential amplifier and a series of analog filters with passbandsthat pass the high and low-frequency components that contribute to theECG waveform 28, but filter out components associated with electricaland mechanical noise. Also within the box 25 is an accelerometer 12 and,alternatively as described above, a microprocessor for performing theadaptive filtering algorithm. A conventional analog ECG waveform 28,such as that shown in FIG. 20A, features a series of heartbeat-inducedpulses, each characterized by a well-known ‘QRS complex’ that,informally, marks the initial depolarization of the patient's heart. Todetermine RR, a separate IP circuit 27 within the plastic box 25generates a low-amperage current (typically 1-4 mA) that is modulated ata high frequency (typically 50-100 kHz). The current typically passesthrough electrode LL (‘lower left’) 24, which is located on the lowerleft-hand side of the patient's torso. It then propagates through thepatient's chest, as indicated by the arrow 29, where arespiration-induced capacitance change modulates it according to the RR.Electrode UR (‘upper right’) 20 detects the resultant analog signal,which is then processed with a separate differential amplifier andseries of analog filters within the IP circuit to determine an analog IPwaveform 30 featuring a low-frequency series of pulses corresponding toRR. Typically the analog filters in the IP circuit 27 are chosen tofilter out high-frequency components that contribute to the ECG QRScomplex.

In other embodiments, the plastic box includes a temperature sensor 33,such as a conventional thermocouple, that measures the skin temperatureof the patient's chest. This temperature is typically a few degreeslower than conventional core temperature, usually measured with athermometer inserted in the patient's throat or rectum. Despite thisdiscrepancy, skin temperature measured with the temperature sensor 33can be monitored continuously and can therefore be used along with RRand other vital signs to predict patient decompensation.

In a preferred embodiment, both the ECG 28 and IP 30 waveforms aregenerated with a single application-specific integrated circuit (ASIC),or a circuit composed of a series of discrete elements which are knownin the art. Preferably the ECG circuit includes an internalanalog-to-digital converter that digitizes both waveforms beforetransmission to the wrist-worn transceiver for further processing. Thiscircuitry, along with that associated with both the ECG and IP circuits,is contained within a single, small-scale electronic package.

Transmission of digital IP, ECG, and ACC waveforms, along with processedRR values, has several advantages over transmission of analog waveforms.First, a single transmission line in the monitor's cabling can transmitmultiple digital waveforms, each generated by different sensors. Thisincludes multiple ECG waveforms 28 (corresponding, e.g., to vectorsassociated with three, five, and twelve-lead ECG systems) from the ECGcircuit 26, the IP waveform 30 from the IP circuit 27, and ACC waveforms19 associated with the x, y, and z axes of accelerometers 10, 12attached to the patient's chest. Limiting the transmission line to asingle cable reduces the number of wires attached to the patient,thereby decreasing the weight and cable-related clutter of the body-wornmonitor. Second, cable motion induced by an ambulatory patient canchange the electrical properties (e.g. electrical impendence) of itsinternal wires. This, in turn, can add noise to an analog signal andultimately the vital sign calculated from it. A digital signal, incontrast, is relatively immune to such motion-induced artifacts. Moresophisticated ECG circuits can plug into the wrist-worn transceiver toreplace the three-lead system shown in FIG. 2A. These ECG circuits caninclude, e.g., five and twelve leads.

Digital data streams are typically transmitted to the wrist-worntransceiver using a serial protocol, such as a controlled area network(CAN) protocol, USB protocol, or RS-232 protocol. CAN is the preferredprotocol for the body-worn monitor described in FIGS. 27A, 27B.

Determining RR from ACC Waveforms

Accelerometers positioned in the above-described locations on thepatient's torso can detect respiration-induced motion associated withthe chest and abdomen, and can therefore be processed to determine RR.Digital filtering is typically required to remove unwanted noise fromthe ACC waveform and isolate signal components corresponding to RR. Goodfiltering is required since respiratory-induced motions are typicallysmall compared to those corresponding to activities (e.g. walking,falling) and posture changes (e.g. standing up, sitting down) associatedwith a patient's motion. Often these signals are only slightly largerthan the accelerometer's noise floor.

FIGS. 3A-3D show a common, normalized ACC waveform without any filtering(FIG. 3A), and then filtered with a progressively narrow digitalbandpass filter generated from a finite impulse response functionfeaturing 1048 coefficients. FIGS. 3E-3H show the first derivative ofthese waveforms, and feature a zero-point crossing corresponding to apositive-to-negative slope change of a single pulse in the ACC waveform.This feature can be easily analyzed with a computer algorithm to countthe various pulses that contribute to RR. As shown in FIG. 3A (the topfigure), an unfiltered ACC waveform typically includes a series ofrespiration-induced pulses characterized by a peak amplitude which, inthis case, is roughly twice that of the noise floor. This poorsignal-to-noise ratio yields a derivatized signal in FIG. 3E that has nodiscernible zero-point crossing, thus making it nearly impossible toanalyze. As shown in FIG. 3B, a relatively wide bandpass filter (0.01→1Hz) yields an ACC waveform with a significantly improved signal-to-noiseratio. Still, as shown in FIG. 3F, the derivative of this waveformfeatures a primary zero-point crossing occurring near 25 seconds, and aseries of artificial noise-induced crossings, both before and after theprimary crossing, that could be erroneously counted by an algorithm toyield an artificially high value for RR.

FIGS. 3C and 3G show, respectively, an ACC waveform and correspondingfirst derivative that result from a relatively narrow 0.01→0.5 Hzbandpass filter. These signals have higher signal-to-noise ratios thanthose shown in FIGS. 3B, 3F, but still include artificial zero-pointcrossings on both sides of the primary zero-point crossing. While small,these features still have the potential to yield an artificially highvalue for RR. The signals shown in FIGS. 3D, 3H, in contrast, are ideal.Here, a narrow 0.01→0.1 Hz bandpass filter removes high-frequencycomponents associated with artifacts in the ACC waveform, and in theprocess removes similar frequency components that contribute to sharprising and falling edges of the individual breathing-induced pulses.This generates a smooth, sinusoid-shaped pulse train that oncederivatized, as shown in FIG. 3H, yields a clean signal with only asingle zero-point crossing. An algorithm can easily analyze this todetermine RR. Importantly, as indicated by the alignment of the primaryzero-point crossing in FIGS. 3F, 3G, and 3H, the finite impulse responsefunction introduces little or no phase shift in the ACC waveforms.

As shown in FIGS. 4-9, under ideal conditions RR determined from afiltered ACC waveform agrees well with that determined from IP, which isa signal used during the adaptive filtering algorithm described herein,and et-CO2, which represents a ‘quasi’ gold standard for determining RR.Data shown in each of these figures were collected simultaneously. ACCand IP waveforms were collected using an accelerometer mounted on apatient's abdomen, similar to that shown in FIG. 1A, and a trio ofelectrodes mounted in an Einthoven's triangle configuration, similar tothat shown in FIG. 2A. The IP waveform is unfiltered, while the ACCwaveform is filtered with a 0.01→0.1 Hz bandpass filter, as describedwith reference to FIGS. 3A, 3H. et-CO2 was measured with a separatesensor positioned within the patient's mouth; signals from this sensorwere not filtered in any way. In all cases breathing-induced pulsescorresponding to RR were determined manually, and are marked accordinglyin the figures. Numerical values within the markers indicate the exactnumber of counted pulses.

FIGS. 4-9 indicate that RR determined from both IP and ACC waveformscorrelates well to absolute RR determined from et-CO2. The correlationholds for a variety of breathing conditions, ranging from slow, deepbreathing (FIGS. 4A-4C); fast, deep breathing (FIGS. 5A-5C); very fast,deep breathing (FIGS. 6A-6C); and shallow, slow breathing (FIGS. 7A-7C).Data were measured under these conditions from a patient in a prone(i.e. lying down) posture. Additionally, the agreement continues to holdfor a standing patient undergoing deep, slow breathing (FIG. 8A-8C) anddeep, fast breathing (FIG. 9A-9C). Even with this range ofconfigurations, RR determined from both ACC and IP waveforms agreed towithin 1 breath/minute to that determined from et-CO2. In most cases thefiltered ACC waveform appeared to have a superior signal-to-noise ratiowhen compared to the IP waveform, with the case for slow, deep breathingfor a standing patient (FIGS. 8A-C) being the one exception.

As shown in FIGS. 10-11, agreement between RR calculated from ACC, IP,and et-CO2 waveforms also holds before and after periods of apnea, asindicated by the shaded region 31 in FIGS. 10A-10C (lasting about 10seconds), and region 32 in FIGS. 11A-11C (lasting about 30 seconds). Asshown in FIGS. 10A-10C, for example, the patient exhibited slow, deepbreaths before the period of apnea 31, and fast, deep breathsafterwards. FIGS. 11A-11C show an opposing configuration. Here, thepatient exhibited fast, shallow breaths before the period of apnea, andslow, shallow breaths afterwards. In both cases agreement between RRcalculated from the three unique waveforms was maintained. These data,as described in more detail below, indicate that an adaptive filteringapproach utilizing both ACC and IP waveforms can be used to predict a RRthat correlates well to that measured with a gold standard, such aset-CO2.

One confounding situation occurs when the patient is walking, as shownin FIGS. 12A-C. Here, in the ACC waveform, signals corresponding to thewalking motion overwhelm those corresponding to breathing, making itimpossible to selectively determine RR. However, the walking motionresults in a well-defined, periodic signal characterized by a very highsignal-to-noise ratio. The IP signal, in contrast, is completelycorrupted by random noise, presumably caused by a combination ofmovements associated with the electrodes and their wires, electricalnoise due to motion of the underlying muscles, and general corruption ofthe underlying capacitance in the patient's torso. This makes itimpossible to determine RR or any other mechanical/physiological statecorresponding to the patient. In this case RR determined from the et-CO2waveform is somewhat noisy, but still discernible.

While impossible to determine RR from the ACC and IP waveforms shown inFIG. 12A-B, the ACC waveform can be analyzed to determine walking, whichit turn may be processed to avoid triggering a false alarm/alert thatwould normally be generated with a conventional vital sign monitor fromthe IP waveform, alone. For example, the ACC waveform shown in FIG. 12A,particularly when coupled with ACC waveforms corresponding to other axesof the chest-worn accelerometer as well as those from otheraccelerometers in the body-worn monitor, shows a clear signal indicativeof walking. This determination can be corroborated with the IP waveform,which for a walking patient features an uncharacteristically lowsignal-to-noise ratio. Based on these signal inputs, an algorithm candetermine that the patient is indeed walking, and can assume that theirRR value is within normal limits, as a patient undergoing a consistentwalking pattern is likely not in dire need of medical attention. Forthis reason an alarm/alert associated with RR is not generated. Similaralarms can be avoided when processing of the ACC waveforms determinesthat the patient is convulsing or falling (see, e.g., FIGS. 21-24),although in these cases a different type of alarm/alert may sound. Inthis way, collective processing of both the ACC and IP waveforms canhelp reduce false alarms/alerts associated with RR, while improving realalarms/alerts corresponding to other patient situations.

Adaptive Filtering

FIG. 13 illustrates in more detail how ACC and IP waveforms can becollectively processed to determine RR, activity levels, posture, andalarms/alerts associated with these patient states. The figure shows aflow chart describing an algorithm that would typically run using amicroprocessor, such as that contained within a wrist-worn transceiversuch as that shown in FIG. 28. Alternatively, the algorithm could run ona microprocessor mounted on the patient's torso with the IP andaccelerometer sensors or elsewhere. The algorithm begins with steps 54,56 that process all nine ACC waveforms, which are shown in the graph 69on the left-hand side of the figure, to determine the patient's posture(step 54) and activity level (step 56). Both these processes aredescribed in detail below. In general, determining posture (step 54)involves processing DC values of the ACC waveform generated by theaccelerometer mounted on the patient's chest; such signals are shown inthe initial and end portions of the graph 69, which show changing DCvalues representing a posture change. Once sampled, the DC values areprocessed with an algorithm to estimate states corresponding to thepatient such as standing, sitting, prone, supine, and lying on theirside. This algorithm is also described with reference to FIG. 26A, 26B,below.

Once posture is determined, the algorithm then analyzes AC portions ofthe ACC waveforms to determine the patient's activity level (step 56).This part of the algorithm, which is also described in detail below, canbe performed in several ways. For example, the AC portions of the ACCwaveforms, such as the oscillating portion in the graph 69, can beprocessed with a Fourier Transform-based analysis to determine afrequency-dependent power-spectrum. Specific activity levels, such aswalking and convulsing, involve periodic or quasi-periodic motions;these result in a well-defined power spectrum with frequency componentsbetween about 0 and 15 Hz (with this value representing the upper limitof human motion). Frequency bands in the power spectrum can be analyzedto estimate the patient's activity level. This analysis can also becombined with the posture determination from step 54 to refine thecalculation for activity level. For example, a patient that is sittingdown may be convulsing, but cannot be walking. Similarly, a fallingevent will begin with a standing posture, and end with a prone or supineposture.

Alternatively, the patient's activity level may be estimated with analgorithm based on probability and the concept of a ‘logit variable’,which considers a variety of time and frequency-domain parametersextracted from the AC portions of the ACC waveforms, and then processesthese with a probability analysis that considers activity levels from apreviously measured group of patients. An analysis based on a series of‘decision trees’ can also be used to estimate the patient's activitylevel. Here, the decision trees feature steps that process both the ACand DC portions of the ACC waveforms to estimate the patient's activitylevel.

Algorithms that describe the patient's posture and activity level aredescribed in detail in the following co-pending patent applications, thecontents of which are incorporated herein by reference: VITAL SIGNMONITOR FEATURING 3 ACCELEROMETERS (U.S. Ser. No. 12/469,094; filed May20, 2009) and METHOD FOR GENERATING ALARMS/ALERTS BASED ON A PATIENT'SPOSTURE AND VITAL SIGNS (U.S. Ser. No. 12/469,236; filed May 20, 2009).

The patient's overall state is preferably grouped into one of twocategories once posture and activity level are determined with steps 54and 56. The first group involves relatively motion-free states, andincludes categories such as patients that are: lying down with minimalmotion (step 58), sitting up with minimal motion (step 59), and standingupright with minimal motion (step 60). Adaptive filtering that processesboth ACC and IP waveforms will be effective in determining RR from thisgroup of patients. The second group features patients that areundergoing some type of motion that will likely influence both the ACCand IP waveforms. Categories for this group include patients that are:lying down with significant motion, e.g. convulsing or talking in ananimated manner (step 61), sitting up with significant motion (step 62),or standing upright with significant motion, e.g. walking (step 63).Here, the adaptive filtering approach is abandoned, as a pair ofrespiratory-influenced waveforms with high signal-to-noise ratios is notavailable. Instead, the second group of patients is processed with aseries of heuristic rules, described above, to determine whether or notto generate an alarm/alert based on their posture, activity level, andvital signs (including RR).

Patients within the first group (steps 58, 59, 60) yield ACC and IPwaveforms that are collectively processed with an algorithm based onadaptive filtering to determine RR. Representative waveforms aredescribed above and are shown, for example, by graphs 70, 71, as well asthose shown in FIGS. 4-11. Details of the adaptive filtering algorithmare described below with reference to FIG. 14. This technique yields anaccurate value for RR (step 66). An alarm/alert is generated if thisvalue exceeds pre-set high and low limits for RR for a well-definedperiod of time (step 67).

For the second group of patients undergoing motion (steps 61, 62, 63) itis assumed that RR is normal but cannot be accurately determined (step65). The underlying theory is that a patient that is walking or talkinglikely has a normal RR, and that such activity levels may result inartificially high or low values of RR that may trigger a false alarm.Still, an alarm/alert may be generated depending on the patient'sposture or activity level, coupled with other vital signs and a set ofheuristic rules (step 68). For example, activity levels such asconvulsing or falling will automatically generate an alarm/alert. Inanother example, during step 68 the algorithm may ignore vital signsthat are known to be strongly affected by motion (e.g. RR, bloodpressure, and SpO2), and process only those that are relatively immuneto motion (e.g. heart rate and temperature). An alarm/alert may betriggered based on these parameters and the patient's motion andactivity level. The set of heuristic rules used during step 68, alongwith a general approach for generating alarms/alerts with the body-wornmonitor described herein, are described in more detail in the followingco-pending patent application, the contents of which have been fullyincorporated by reference above: METHOD FOR GENERATING ALARMS/ALERTSBASED ON A PATIENT'S POSTURE AND VITAL SIGNS (U.S. Ser. No. 12/469,236;filed May 20, 2009).

FIG. 14 describes in more detail an exemplary adaptive filteringalgorithm used during step 64 to determine RR from the IP and ACCwaveforms. The algorithm involves collecting ECG, PPG, ACC, and IPwaveforms using the body-worn monitor described in FIGS. 27A, B (step81). ECG and PPG waveforms are processed with external algorithms todetermine heart rate, blood pressure, and pulse oximetry, as describedin more detail below. Additionally, as described with reference to FIGS.20A-E, these waveforms feature envelopes that are modulated byrespiratory rate, and thus may be analyzed to provide an initial RRvalue for the adaptive filtering algorithm. Once collected, the ECG,PPG, and IP waveforms are analyzed with a series of simple metrics, suchas analysis of signal-to-noise ratios and comparison of extracted RRvalues to pre-determined limits, to determine which one will provide theinitial input to the adaptive filtering algorithm (step 82). Ideally RRis extracted from the IP waveform, as this provides a reliable initialvalue. If during step 82 it is determined that IP does not yield areliable initial RR value, the envelopes of both the PPG and ECGwaveforms are extracted and analyzed as described above. If they areacceptable, RR values are then extracted from these waveforms and usedfor the initial value (step 89). The algorithm is terminated if each ofthe IP, PPG, and ECG waveforms fails to yield a reliable RR value.

If the IP waveform is deemed suitable, it is filtered with a finiteimpulse response filter with a bandpass of 0.01→12 Hz to removeelectrical and mechanical noise that may lead to artifacts (step 83).Once filtered, the waveform is derivatized to yield a waveform similarto that shown in FIG. 3H (step 84), and then analyzed to find azero-point crossing so that peaks corresponding to RR can be counted(step 85). During step 85 several simple signal processing algorithmsmay also be deployed to avoid counting features that don't actuallycorrespond to RR, such as those shown in FIGS. 3F, 3G. For example,prior to looking for the zero-point crossing, the derivatized waveformmay be squared to accentuate lobes on each side of the crossing. Theresultant waveform may then be filtered again with a bandpass filter, orsimply smoothed with a moving average. In other embodiments only lobesthat exceed a pre-determined magnitude are considered when determiningthe zero-point crossing.

Once determined during step 85, the initial RR serves as the basis forthe adaptive filter used in step 85. Typically this rate is multipliedby a factor (e.g. 1.5), and then used as an upper limit for a bandpassfilter based on a finite impulse response function used to filter theACC waveform (step 86). The lower limit for the bandpass filter istypically 0.01 Hz, as described above. Filtering the ACC waveform withthese tailored parameters yields a resulting waveform that has a highsignal-to-noise ratio, limited extraneous frequency components, and caneasily be processed to determine RR. During step 87 signal processingtechnique similar to those described above with reference to step 84 maybe used to further process the ACC waveform. These yield a smooth,derivatized waveform that is analyzed to determine a zero-point crossingand count the resulting peaks contributing to RR (step 88).

FIGS. 15, 16, and 17 illustrate how the above-described adaptivefiltering algorithm can be applied to both ACC and IP waveforms. In eachof the figures, the graphs show the ACC waveform filtered with aninitial, non-adaptive filter (15A, 16A, 17A; 0.01→2 Hz bandpass), andthe IP waveform filtered under similar conditions with a slightly largerbandpass filter (15B, 16B, 17B; 0.01→12 Hz bandpass). Typically the IPwaveform is filtered with the larger bandpass so that high-frequencycomponents composing the rising and falling edges of pulses within thesewaveforms are preserved.

Once filtered, the IP waveform is processed as described above todetermine an initial RR. This value may include artifacts due to motion,electrical, and mechanical noise that erroneously increases or decreasesthe initial RR value. But typically such errors have little impact onthe final RR value that results from the adaptive filter. The middlegraph (FIGS. 15C, 16C, and 17C) in each figure show the ACC waveformprocessed with the adaptive filter. In all cases this waveform featuresan improved signal-to-noise ratio compared to data shown in the topgraph (15A, 16A, 17A), which is processed with a non-adaptive (andrelatively wide) filter. Typically the narrow bandpass on the adaptivefilter removes many high-frequency components that contribute the sharprising and falling edges of pulses in the ACC waveforms. This slightlydistorts the waveforms by rounding the pulses, giving the filteredwaveform a shape that resembles a conventional sinusoid. Suchdistortion, however, has basically no affect on the absolute number ofpulses in each waveform which are counted to determine RR.

The adaptively filtered waveform is then derivatized and graphed inFIGS. 15D, 16D, and 17D. This waveform is then processed with theabove-mentioned signal processing techniques, e.g. squaring thederivative and filtering out lobes that fall beneath pre-determinedthreshold values, to yield an algorithm-determined ‘count’, indicated inFIGS. 15E, 16E, and 17E as a series of black triangles. The count isplotted along with the adaptively filtered waveforms from FIGS. 15C,16C, and 17C. Exact overlap between each pulse in the waveform and thecorresponding count indicates the algorithm is working properly. Datafrom each of the figures correspond to varying respiratory behavior (5,17, and 38 breaths/minute in, respectively, FIGS. 15, 16, and 17), andindicate that this technique is effective over a wide range of breathingfrequencies. The right-hand side of the figures (FIGS. 15F, 16F, and17F) show a series of steps 90-94 that indicate the analysis required togenerate the corresponding graphs in the figure.

FIG. 18 shows data collected when the patient is walking. Here, thewalking motion manifests in the ACC waveform in FIG. 18A as a series ofperiodic pulses which look similar to RR, particularly after the initialbandpass filter of 0.01→2 Hz. However, the IP waveform shown in FIG. 18Bhas a poor signal-to-noise ratio, and fails to yield an accurate initialvalue for RR. This is indicated by step 95 in the modified flow chartshown in FIG. 18C, which highlights an alternate series of steps thatare deployed when motion is present. As shown in step 96, in this caseother ACC waveforms (e.g., those along the x and y-axes, indicated byACC′) are analyzed to determine that the patient is walking. In thiscase no value of RR is reported, and an alarm/alert is not triggeredbecause of the above-mentioned heuristic rules (i.e. a walking patienttypically has a normal RR, and is not in need of medical attention).

The efficacy of using adaptive filtering to determine RR from ACC and IPwaveforms is summarized with the correlation graph in FIG. 19. The graphshows correlation with et-CO2, which in this case represents a goldstandard. Correlation is strong (r^2=0.99 for a RR range of 5-54breaths/minute), and the graph includes data collected from patients ina range of postures (standing upright, lying down) and undergoing arange of breathing behaviors (deep breaths, shallow breaths). Biascalculated from these data was 0.8 breaths/minute, and the standarddeviation of the differences was 1.6 breaths/minute. These statisticsindicate adaptive filtering yields RR with an accuracy that is withinthe FDA's standards of +/−2 breaths/minute over a range of 0-70breaths/minute.

Determining Respiratory Rate from ECG and PPG Waveforms

As described above, RR can additionally be determined from both the PPGand ECG waveforms by analyzing an envelope outlining heartbeat-inducedpulses in these waveforms. Both PPG and ECG waveforms are collected withthe body-worn monitor of FIGS. 27A, 27B, where they are further analyzedto continuously determine cNIBP according to the Composite Technique, asdescribed above. FIGS. 20A-E show representative data that indicate thistechnique. FIG. 20A, for example, shows an unfiltered ECG waveformfeaturing a train of pulses, each representing an individual QRScomplex. The envelope of the QRS complexes is extracted by determiningthe maximum and minimum of each complex. Alternatively it can bedetermined with a series of digital filters that only pass very lowfrequencies. Comparison of the ECG envelope in FIG. 20B with the IPwaveform in FIG. 20E indicates good agreement between these twoapproaches. Similarly, the PPG waveform shown in FIG. 20C features atrain of pulses, each corresponding to a unique heartbeat, thattypically follow the ECG QRS complex by a few hundred milliseconds. Itis this time difference (typically called a ‘pulse transit time’, orPTT) that is sensitive to blood pressure changes, and is used during theComposite Technique to measure an absolute value for blood pressure. ThePPG envelope, like the ECG envelope, is modulated by RR, and can bedetermined by extracting the maximum and minimum of each pulse.Alternatively this envelope can be determined with a low-pass filtersimilar to that used to extract the ECG envelope. As shown in FIG. 20D,the resulting envelope agrees well with the IP waveform, indicating ittoo is indicative of RR.

The body-worn monitor shown in FIGS. 27A, 27B measures two separate PPGwaveforms (generated with red and infrared radiation) to determine thepatient's SpO2 value. The algorithm for this calculation is described indetail in the following co-pending patent applications, the contents ofwhich are incorporated herein by reference:BODY-WORN PULSE OXIMETER(U.S. Ser. No. 61/218,062; filed Jun. 17, 2009). In embodiments,envelopes from both PPG waveforms can be extracted and processed todetermine an initial value of RR. This value may also be calculated fromthe ECG waveform alone, or from this waveform and one or both PPGwaveforms. As described above, this method for determining an initial RRvalue for the adaptive filter algorithm is less preferred than one thatuses an IP waveform. Such an algorithm would be used, for example, if anIP waveform featuring a good signal-to-noise ratio was not available.

Affect of Motion on ECG, PPG, and ACC Waveforms

A patient's activity level, as characterized by ACC waveforms, can havea significant impact on the PPG and ECG waveforms used to measure RR andcNIBP. For example, FIGS. 21-24 show time-dependent graphs of ECG, PPG,and ACC waveforms for a patient who is resting (FIG. 21), walking (FIG.22), convulsing (FIG. 23), and falling (FIG. 24). Each graph includes asingle ECG waveform, PPG waveform and three ACC waveforms. In all casesthe PPG waveforms are generated with the infrared light source. The ACCwaveforms correspond to signals measured along the x, y, and z axes by asingle accelerometer worn on the patient's wrist, similar to theaccelerometer used within the wrist-worn transceiver shown in FIG. 28.

The figures indicate that time-dependent properties of both ECG and PPGwaveforms can be strongly affected by certain patient activities, whichare indicated by the ACC waveforms. Accuracy of RR and cNIBP calculatedfrom these waveforms is therefore affected as well. FIGS. 21A-C, forexample, shows data collected from a patient at rest. This state isclearly indicated by the ACC waveforms (FIG. 21C; bottom), which featurea relatively stable baseline along all three axes of the accelerometer.High-frequency noise in all the ACC waveforms shown in FIGS. 21-24 isdue to electrical noise, and is not indicative of patient motion in anyway. The ECG (FIG. 21A; top) and PPG (FIG. 21B; middle) waveforms forthis patient are correspondingly stable, thus allowing algorithmsoperating on the body-worn monitor to accurately determine SpO2 (fromthe PPG waveform), along with heart rate and respiratory rate (from theECG waveform), cNIBP (from a PTT extracted from both the ECG and PPGwaveforms). Based on the data shown in FIG. 21, algorithms operating onthe body-worn monitor assume that vital signs calculated from a restingpatient are relatively stable; the algorithm therefore deploys normalthreshold criteria for alarms/alerts, described below in Table 1, forpatients in this state.

The ECG and PPG waveforms shown, respectively, in FIGS. 21A and 21B alsofeature envelopes indicated by the dashed lines 97 a, 97 b, 98 that aremodulated by RR. This modulation is similar to that shown in FIGS. 20Aand 20C.

FIGS. 22A-C shows ECG (FIG. 22A; top), PPG (FIG. 22B; middle), and ACC(FIG. 22C; top) waveforms measured from a walking patient wearing thebody-worn monitor. In this case, the ACC waveform clearly indicates aquasi-periodic modulation, with each ‘bump’ in the modulationcorresponding to a particular step. The ‘gaps’ in the modulation, shownnear 10, 19, 27, and 35 seconds, correspond to periods when the patientstops walking and changes direction. Each bump in the ACC waveformincludes relatively high-frequency features (other than those associatedwith electrical noise, described above) that correspond towalking-related movements of the patient's wrist.

The ECG waveform measured from the walking patient is relativelyunaffected by motion, other than indicating an increase in heart rate(i.e., a shorter time separation between neighboring QRS complexes) andrespiratory rate (i.e. a higher frequency modulation of the waveform'senvelope) caused by the patient's exertion. The PPG waveform, incontrast, is strongly affected by this motion, and pulses within itbecome basically immeasurable. Its distortion is likely due in part to aquasi-periodic change in light levels, caused by the patient's swingingarm, and detected by the photodetector within the thumb-worn sensor.Movement of the patient's arm additionally affects blood flow in thethumb and can cause the optical sensor to move relative to the patient'sskin. The photodetector measures all of these artifacts, along with aconventional PPG signal (like the one shown in FIG. 21B) caused byvolumetric expansion in the underlying arteries and capillaries withinthe patient's thumb. The artifacts produce radiation-inducedphotocurrent that is difficult to distinguish from normal PPG signalused to calculate SpO2 and cNIBP. These vital signs are thus difficultor impossible to accurately measure when the patient is walking.

The body-worn monitor may deploy multiple strategies to avoid generatingfalse alarms/alerts during a walking activity state that correspond toRR as well as all other vital signs. As described in detail below, themonitor can detect this state by processing the ACC waveforms shown inFIG. 22C along with similar waveforms measured from the patient's bicepand chest. Walking typically elevates heart rate, respiratory rate, andblood pressure, and thus alarm thresholds for these parameters, asindicated by Table 1, are systematically and temporarily increased whenthis state is detected. Values above the modified thresholds areconsidered abnormal, and trigger an alarm. SpO2, unlike heart rate,respiratory rate and blood pressure, does not typically increase withexertion. Thus the alarm thresholds for this parameter, as shown inTable 1, do not change when the patient is walking. Body temperaturemeasured with the body-worn monitor typically increases between 1-5%,depending on the physical condition of the patient and the speed atwhich they are walking.

TABLE 1 motion-dependent alarm/alert thresholds and heuristic rules fora walking patient Modified Motion Threshold for Heuristic Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Walking Increase(+10-30%) Ignore Threshold; Do Pressure Not Alarm/Alert (SYS, DIA) HeartRate Walking Increase Use Modified (+10-300%) Threshold; Alarm/Alert ifValue Exceeds Threshold Respiratory Walking Increase Ignore Threshold;Do Rate (+10-300%) Not Alarm/Alert SpO2 Walking No Change IgnoreThreshold; Do Not Alarm/Alert Temperature Walking Increase (+10-30%) UseOriginal Threshold; Alarm/Alert if Value Exceeds Threshold

To further reduce false alarms/alerts, software associated with thebody-worn monitor or remote monitor can deploy a series of heuristicrules determined beforehand using practical, empirical studies. Theserules, for example, can indicate that a walking patient is likelyhealthy, breathing, and characterized by a normal RR. Accordingly, therules dictate that cNIBP, RR, and SpO2 values measured during a walkingstate that exceed predetermined alarm/alert thresholds are likelycorrupted by artifacts; the system, in turn, does not sound thealarm/alert in this case. Heart rate, as indicated by FIG. 22A, and bodytemperature can typically be accurately measured even when a patient iswalking; the heuristic rules therefore dictate the modified thresholdslisted in Table 1 be used to generate alarms/alerts for a patient inthis state.

Additionally, despite the patient's walking motion, the ECG waveformshown in FIG. 22A still features an envelope shown by the dashed lines99 a, 99 b that represents the patient's RR. This indicates that RR maybe determined from a walking patient by processing the ECG envelope,even when other signals (e.g. IP and ACC waveforms) are corrupted.Because of the motion-induced noise in these signals, RR is typicallydetermined directly from the ECG envelope, without using any adaptivefiltering.

FIGS. 23A-C show ECG (FIG. 23A; top), PPG (FIG. 23B; middle), and ACC(FIG. 23C; bottom) waveforms measured from a patient that is simulatingconvulsing by rapidly moving their arm back and forth. A patientundergoing a Gran-mal seizure, for example, would exhibit this type ofmotion. As is clear from the waveforms, the patient is at rest for theinitial 10 seconds shown in the graph, during which the ECG and PPGwaveforms are uncorrupted by motion. The patient then begins a period ofsimulated, rapid convulsing that lasts for about 12 seconds. A brief5-second period of rest follows, and then convulsing begins for another12 seconds or so.

Convulsing modulates the ACC waveform due to rapid motion of thepatient's arm, as measured by the wrist-worn accelerometer. Thismodulation is strongly coupled into the PPG waveform, likely because ofthe phenomena described above, i.e.: 1) ambient light coupling into theoximetry probe's photodiode; 2) movement of the photodiode relative tothe patient's skin; and 3) disrupted blow flow underneath the probe.Note that from about 23-28 seconds the ACC waveform is not modulated,indicating that the patient's arm is at rest. During this period theambient light is constant and the optical sensor is stationary relativeto the patient's skin. But the PPG waveform is still strongly modulated,albeit at a different frequency than the modulation that occurred whenthe patient's arm was moving, and the pulses therein are difficult toresolve. This indicates that the disrupted blood flow underneath theoptical sensor continues even after the patient's arm stops moving.Using this information, both ECG and PPG waveforms similar to thoseshown in FIG. 23 can be analyzed in conjunction with ACC waveformsmeasured from groups of stationary and moving patients. These data canthen be analyzed to estimate the effects of specific motions andactivities on the ECG and PPG waveforms, and then deconvolute thesefactors using known mathematical techniques to effectively remove anymotion-related artifacts. The deconvoluted ECG and PPG waveforms canthen be used to calculate vital signs, as described in detail below.

The ECG waveform is modulated by the patient's arm movement, but to alesser degree than the PPG waveform. In this case, modulation is causedprimarily by electrical ‘muscle noise’ instigated by the convulsion anddetected by the ECG electrodes, and well as by convulsion-induced motionin the ECG cables and electrodes relative to the patient's skin. Suchmotion is expected to have a similar affect on temperature measurements,which are determined by a sensor that also includes a cable.

Table 2, below, shows examples of the modified threshold values andheuristic rules for alarms/alerts generated by a convulsing patient. Ingeneral, when a patient experiences convulsions, such as those simulatedduring the two 12-second periods in FIG. 23, it is virtually impossibleto accurately measure any vital signs from the ECG and PPG waveforms.For this reason the threshold values corresponding to each vital signare not adjusted when convulsions are detected. Heart rate determinedfrom the ECG waveform, for example, is typically erroneously high due tohigh-frequency convulsions, and RR is immeasurable from the distortedwaveform. Strong distortion of the optical waveform also makes both SpO2and PPT-based cNIBP difficult or impossible to measure. For this reason,algorithms operating on either the body-worn monitor or a remote monitorwill not generate alarms/alerts based on vital signs when a patient isconvulsing, as these vital signs will almost certainly be corrupted bymotion-related artifacts.

TABLE 2 motion-dependent alarm/alert thresholds and heuristic rules fora convulsing patient Modified Motion Threshold for Heuristic Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure ConvulsingNo Change Ignore Threshold; (SYS, DIA) Generate Alarm/Alert Because ofConvulsion Heart Rate Convulsing No Change Ignore Threshold; GenerateAlarm/Alert Because of Convulsion Respiratory Rate Convulsing No ChangeIgnore Threshold; Generate Alarm/Alert Because of Convulsion SpO2Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because ofConvulsion Temperature Convulsing No Change Ignore Threshold; GenerateAlarm/Alert Because of Convulsion

Table 2 also shows exemplary heuristic rules for convulsing patients.Here, the overriding rule is that a convulsing patient needs assistance,and thus an alarm/alert for this patient is generated regardless oftheir vital signs (which, as described above, are likely inaccurate dueto motion-related artifacts). The system always generates an alarm/alertfor a convulsing patient.

FIGS. 24A-C shows ECG (FIG. 24A; top), PPG (FIG. 24B; middle), and ACC(FIG. 24C; bottom) waveforms measured from a patient that experiences afall roughly 13 seconds into the measuring period. The ACC waveformclearly indicates the fall with a sharp decrease in its signal, followedby a short-term oscillatory signal, due (literally) to the patientbouncing on the floor. After the fall, ACC waveforms associated with thex, y, and z axes also show a prolonged decrease in value due to theresulting change in the patient's posture. In this case, both the ECGand PPG waveforms are uncorrupted by motion prior to the fall, butbasically immeasurable during the fall, which typically takes only 1-2seconds. Specifically, this activity adds very high frequency noise tothe ECG waveform, making it impossible to extract heart rate and RRduring this short time period. Falling causes a sharp drop in the PPGwaveform, presumably for the same reasons as described above (i.e.changes in ambient light, sensor movement, and disruption of blood flow)for walking and convulsing, making it difficult to measure SpO2 andcNIBP.

After a fall, both the ECG and PPG waveforms are free from artifacts,but both indicate an accelerated heart rate and relatively high heartrate variability for roughly 10 seconds. During this period the PPGwaveform also shows distortion and a decrease in pulse amplitude.Without being bound to any theory, the increase in heart rate may be dueto the patient's baroreflex, which is the body's haemostatic mechanismfor regulating and maintaining blood pressure. The baroreflex, forexample, is initiated when a patient begins faint. In this case, thepatient's fall may cause a rapid drop in blood pressure, therebydepressing the baroreflex. The body responds by accelerating heart rate(indicated by the ECG waveform) and increasing blood pressure (indicatedby a reduction in PTT, as measured from the ECG and PPG waveforms) inorder to deliver more blood to the patient's extremities.

Table 3 shows exemplary heuristic rules and modified alarm thresholdsfor a falling patient. Falling, similar to convulsing, makes itdifficult to measure waveforms and the vital signs calculated from them.Because of this and the short time duration associated with a fall,alarms/alerts based on vital signs thresholds are not generated duringan actual falls. However, this activity, optionally coupled withprolonged stationary period or convulsion (both determined from thefollowing ACC waveform), generates an alarm/alert according to theheuristic rules.

TABLE 3 motion-dependent alarm/alert thresholds and heuristic rules fora falling patient Processing ACC Waveforms to Determine Posture ModifiedMotion Threshold for Heuristic Rules for Vital Sign State Alarms/AlertsAlarms/Alerts Blood Pressure Falling No Change Ignore Threshold;Generate (SYS, DIA) Alarm/Alert Because of Fall Heart Rate Falling NoChange Ignore Threshold; Generate Alarm/Alert Because of FallRespiratory Rate Falling No Change Ignore Threshold; GenerateAlarm/Alert Because of Fall SpO2 Falling No Change Ignore Threshold;Generate Alarm/Alert Because of Fall Temperature Falling No ChangeIgnore Threshold; Generate Alarm/Alert Because of Fall

In addition to activity level, as described above and indicated in FIGS.21-24, a patient's posture can influence how the above-described systemgenerates alarms/alerts from RR, cNIBP, and other vital signs. Forexample, the alarms/alerts related to both RR and cNIBP may varydepending on whether the patient is lying down or standing up. FIG. 25indicates how the body-worn monitor can determine motion-relatedparameters (e.g. degree of motion, posture, and activity level) from apatient 110 using time-dependent ACC waveforms continuously generatedfrom the three accelerometers 112, 113, 114 worn, respectively, on thepatient's chest, bicep, and wrist. The height of the patient's arm canaffect the cNIBP measurement, as blood pressure can vary significantlydue to hydrostatic forces induced by changes in arm height. Moreover,this phenomenon can be detected and exploited to calibrate the cNIBPmeasurement, as described in detail in the above-referenced patentapplication, the contents of which have been previously incorporated byreference:BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING ANDANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009). Asdescribed in this document, arm height can be determined using DCsignals from the accelerometers 113, 114 disposed, respectively, on thepatient's bicep and wrist. Posture, in contrast, can be exclusivelydetermined by the accelerometer 112 worn on the patient's chest. Analgorithm operating on the wrist-worn transceiver extracts DC valuesfrom waveforms measured from this accelerometer and processes them withan algorithm described below to determine posture.

Specifically, torso posture is determined for a patient 110 using anglesdetermined between the measured gravitational vector and the axes of atorso coordinate space 111. The axes of this space 111 are defined in athree-dimensional Euclidean space where {right arrow over (R)}_(CV) isthe vertical axis, {right arrow over (R)}_(CH) is the horizontal axis,and {right arrow over (R)}_(CN) is the normal axis. These axes must beidentified relative to a ‘chest accelerometer coordinate space’ beforethe patient's posture can be determined.

The first step in determining a patient's posture is to identifyalignment of {right arrow over (R)}_(CV) in the chest accelerometercoordinate space. This can be determined in either of two approaches. Inthe first approach, {right arrow over (R)}_(CV) is assumed based on atypical alignment of the body-worn monitor relative to the patient.During a manufacturing process, these parameters are then preprogrammedinto firmware operating on the wrist-worn transceiver. In this procedureit is assumed that accelerometers within the body-worn monitor areapplied to each patient with essentially the same configuration. In thesecond approach, {right arrow over (R)}_(CV) is identified on apatient-specific basis. Here, an algorithm operating on the wrist-worntransceiver prompts the patient (using, e.g., video instructionoperating on the wrist-worn transceiver, or audio instructionstransmitted through a speaker) to assume a known position with respectto gravity (e.g., standing upright with arms pointed straight down). Thealgorithm then calculates {right arrow over (R)}_(CV) from DC valuescorresponding to the x, y, and z axes of the chest accelerometer whilethe patient is in this position. This case, however, still requiresknowledge of which arm (left or right) the monitor is worn on, as thechest accelerometer coordinate space can be rotated by 180 degreesdepending on this orientation. A medical professional applying themonitor can enter this information using the GUI, described above. Thispotential for dual-arm attachment requires a set of two pre-determinedvertical and normal vectors which are interchangeable depending on themonitor's location. Instead of manually entering this information, thearm on which the monitor is worn can be easily determined followingattachment using measured values from the chest accelerometer values,with the assumption that {right arrow over (R)}_(CV) is not orthogonalto the gravity vector.

The second step in the procedure is to identify the alignment of {rightarrow over (R)}_(CN) in the chest accelerometer coordinate space. Themonitor determines this vector in the same way it determines {rightarrow over (R)}_(CV) using one of two approaches. In the first approachthe monitor assumes a typical alignment of the chest-worn accelerometeron the patient. In the second approach, the alignment is identified byprompting the patient to assume a known position with respect togravity. The monitor then calculates {right arrow over (R)}_(CN) fromthe DC values of the time-dependent ACC waveform.

The third step in the procedure is to identify the alignment of {rightarrow over (R)}_(CH) in the chest accelerometer coordinate space. Thisvector is typically determined from the vector cross product of {rightarrow over (R)}_(CV) and {right arrow over (R)}_(CN), or it can beassumed based on the typical alignment of the accelerometer on thepatient, as described above.

A patient's posture is determined using the coordinate system describedabove and in FIG. 25, along with a gravitational vector {right arrowover (R)}_(G) that extends normal from the patient's chest. The anglebetween {right arrow over (R)}_(CV) and {right arrow over (R)}_(G) isgiven by equation (1):

$\begin{matrix}{{\theta_{VG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CV}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CV}}} \right)}} & (1)\end{matrix}$where the dot product of the two vectors is defined as:{right arrow over (R)} _(G)[n]·{right arrow over (R)} _(CV)=(y_(Cx)[n]×r _(CVx))+(y _(cy)[n]×r _(CVy))+(y _(Cz)[n]λr _(CVz))  (2)The definition of the norms of {right arrow over (R)}_(G) and {rightarrow over (R)}_(CV) are given by equations (3) and (4):∥{right arrow over (R)} _(G)[n]∥=√{square root over ((y _(Cx)[n])²+(y_(Cy)[n])²+(y _(Cz)[n])²)}  (3)∥{right arrow over (R)} _(CV)∥=(r _(CVx))²+(r _(CVy))²+(r _(CVz))²  (4)

As indicated in equation (5), the monitor compares the vertical angleθ_(VG) to a threshold angle to determine whether the patient is vertical(i.e. standing upright) or lying down:if θ_(VG)≤45° then Torso State=0, the patient is upright  (5)If the condition in equation (5) is met the patient is assumed to beupright, and their torso state, which is a numerical value equated tothe patient's posture, is equal to 0. The patient is assumed to be lyingdown if the condition in equation (5) is not met, i.e. θ_(VG)>45degrees. Their lying position is then determined from angles separatingthe two remaining vectors, as defined below.

The angle θ_(VG) between {right arrow over (R)}_(CN) and {right arrowover (R)}_(G) determines if the patient is lying in the supine position(chest up), prone position (chest down), or on their side. Based oneither an assumed orientation or a patient-specific calibrationprocedure, as described above, the alignment of {right arrow over(R)}_(CN) is given by equation (6), where i, j, k represent the unitvectors of the x, y, and z axes of the chest accelerometer coordinatespace respectively:{right arrow over (R)} _(CN) =r _(CNx) {circumflex over (ι)}+r _(CNy)Ĵ+r _(CNZ) {circumflex over (k)}  (6)The angle between {right arrow over (R)}_(CN) and {right arrow over(R)}_(G) determined from DC values extracted from the chestaccelerometer ACC waveform is given by equation (7):

$\begin{matrix}{{\theta_{NG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CN}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CN}}} \right)}} & (7)\end{matrix}$The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position the patient is lying in, as shown in equation (8):if θ_(NG)≤35° then Torso State=1, the patient is supineif θ_(NG)≥135° then Torso State=2, the patient is prone  (8)If the conditions in equation (8) are not met then the patient isassumed to be lying on their side. Whether they are lying on their rightor left side is determined from the angle calculated between thehorizontal torso vector and measured gravitational vectors, as describedabove.

The alignment of {right arrow over (R)}_(CH) is determined using eitheran assumed orientation, or from the vector cross-product of {right arrowover (R)}_(CV) and {right arrow over (R)}_(CN) as given by equation (9),where i, j, k represent the unit vectors of the x, y, and z axes of theaccelerometer coordinate space respectively. Note that the orientationof the calculated vector is dependent on the order of the vectors in theoperation. The order below defines the horizontal axis as positivetowards the right side of the patient's body.{right arrow over (R)} _(CH) =r _(CVx) {right arrow over (ι)}+r _(CVy){right arrow over (J)}+r _(CVz) {circumflex over (k)}={right arrow over(R)} _(CV) ×{right arrow over (R)} _(CN)  (9)The angle θ_(HG) between {right arrow over (R)}_(CH) and {right arrowover (R)}_(G) is determined using equation (10):

$\begin{matrix}{{\theta_{HG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CH}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CH}}} \right)}} & (10)\end{matrix}$The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by equation (11):if θ_(HG)≥90° then Torso State=3, the patient is on their right sideif θ_(NG)<90° then Torso State=4, the patient is on their leftside  (11)Table 4 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon on a remote computer:

TABLE 4 postures and their corresponding torso states Posture TorsoState standing upright 0 supine: lying on back 1 prone: lying on chest 2lying on right side 3 lying on left side 4 undetermined posture 5

FIGS. 26A and 26B show, respectively, graphs of time-dependent ACCwaveforms measured along the x, y, and z-axes (FIG. 26A), and the torsostates (i.e. postures; FIG. 26B) determined from these waveforms for amoving patient, as described above. As the patient moves, the DC valuesof the ACC waveforms measured by the chest accelerometer varyaccordingly, as shown in FIG. 26A. The body-worn monitor processes thesevalues as described above to continually determine {right arrow over(R)}_(G) and the various quantized torso states for the patient, asshown in FIG. 26B. The torso states yield the patient's posture asdefined in Table 4. For this study the patient rapidly alternatedbetween standing, lying on their back, chest, right side, and left sidewithin a time period of about 160 seconds. As described above, differentalarm/alert conditions (e.g. threshold values) for vital signs can beassigned to each of these postures, or the specific posture itself mayresult in an alarm/alert. Additionally, the time-dependent properties ofthe graph can be analyzed (e.g. changes in the torso states can becounted) to determine, for example, how often the patient moves in theirhospital bed. This number can then be equated to various metrics, suchas a ‘bed sore index’ indicating a patient that is so stationary intheir bed that lesions may result. Such a state could then be used totrigger an alarm/alert to the supervising medical professional.

Hardware for Measuring Respiratory Rate

FIGS. 27A and 27B show how the body-worn monitor 200 described aboveattaches to a patient 170 to measure RR, cNIBP, and other vital signs.These figures show two configurations of the system: FIG. 27A shows thesystem used during the indexing portion of the Composite Technique, andincludes a pneumatic, cuff-based system 185, while FIG. 27B shows thesystem used for subsequent RR and cNIBP measurements. The indexingmeasurement typically takes about 60 seconds, and is typically performedonce every 4 hours. Once the indexing measurement is complete thecuff-based system 185 is typically removed from the patient. Theremainder of the time the monitor 200 performs the RR, SpO2 and cNIBPmeasurements.

The body-worn monitor 200 features a wrist-worn transceiver 172,described in more detail in FIG. 28, featuring a touch panel interface173 that displays RR, blood pressure values and other vital signs. Awrist strap 190 affixes the transceiver 172 to the patient's wrist likea conventional wristwatch. A flexible cable 192 connects the transceiver172 to a pulse oximeter probe 194 that wraps around the base of thepatient's thumb. During a measurement, the probe 194 generates atime-dependent PPG waveform which is processed along with an ECG tomeasure cNIBP, SpO2, and possible RR. This provides an accuraterepresentation of blood pressure in the central regions of the patient'sbody, as described above.

To determine ACC waveforms the body-worn monitor 200 features threeseparate accelerometers located at different portions on the patient'sarm and chest. The first accelerometer is surface-mounted on a circuitboard in the wrist-worn transceiver 172 and measures signals associatedwith movement of the patient's wrist. As described above, this motioncan also be indicative of that originating from the patient's fingers,which will affect the SpO2 measurement. The second accelerometer isincluded in a small bulkhead portion 196 included along the span of thecable 182. During a measurement, a small piece of disposable tape,similar in size to a conventional bandaid, affixes the bulkhead portion196 to the patient's arm. In this way the bulkhead portion 196 servestwo purposes: 1) it measures a time-dependent ACC waveform from themid-portion of the patient's arm, thereby allowing their posture and armheight to be determined as described in detail above; and 2) it securesthe cable 182 to the patient's arm to increase comfort and performanceof the body-worn monitor 200, particularly when the patient isambulatory. The third accelerometer is mounted in a bulkhead component174 that connects through cables 180 a-c to ECG electrodes 178 a-c. Asdescribed in detail above, this accelerometer, which can also be mountedcloser to the patient's abdomen, measures respiration-induced motion ofthe patient's chest and abdomen. These signals are then digitized,transmitted through the cable 182 to the wrist-worn transceiver 172,where they are processed with an algorithm as described above todetermine RR.

The cuff-based module 185 features a pneumatic system 176 that includesa pump, valve, pressure fittings, pressure sensor, analog-to-digitalconverter, microcontroller, and rechargeable Li:ion battery. During anindexing measurement, the pneumatic system 176 inflates a disposablecuff 184 and performs two measurements according to the CompositeTechnique: 1) it performs an inflation-based measurement of oscillometryto determine values for SYS, DIA, and MAP; and 2) it determines apatient-specific relationship between PTT and MAP. These measurementsare described in detail in the above-referenced patent applicationentitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USINGOPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser. No. 12/138,194;filed Jun. 12, 2008), the contents of which have been previouslyincorporated herein by reference.

The cuff 184 within the cuff-based pneumatic system 185 is typicallydisposable and features an internal, airtight bladder that wraps aroundthe patient's bicep to deliver a uniform pressure field. During theindexing measurement, pressure values are digitized by the internalanalog-to-digital converter, and sent through a cable 186 according to aCAN protocol, along with SYS, DIA, and MAP blood pressures, to thewrist-worn transceiver 172 for processing as described above. Once thecuff-based measurement is complete, the cuff-based module 185 is removedfrom the patient's arm and the cable 186 is disconnected from thewrist-worn transceiver 172. cNIBP is then determined using PTT, asdescribed in detail above.

To determine an ECG, the body-worn monitor 200 features a small-scale,three-lead ECG circuit integrated directly into the bulkhead 174 thatterminates an ECG cable 182. The ECG circuit features an integratedcircuit that collects electrical signals from three chest-worn ECGelectrodes 178 a-c connected through cables 180 a-c. As described above,the ECG electrodes 178 a-c are typically disposed in a conventionalEinthoven's Triangle configuration which is a triangle-like orientationof the electrodes 178 a-c on the patient's chest that features threeunique ECG vectors. From these electrical signals the ECG circuitdetermines up to three ECG waveforms, which are digitized using ananalog-to-digital converter mounted proximal to the ECG circuit, andsent through the cable 182 to the wrist-worn transceiver 172 accordingto the CAN protocol. There, the ECG and PPG waveforms are processed todetermine the patient's blood pressure. Heart rate and RR are determineddirectly from the ECG waveform using known algorithms, such as thosedescribed above. The cable bulkhead 174 also includes an accelerometerthat measures motion associated with the patient's chest as describedabove.

As described above, there are several advantages of digitizing ECG andACC waveforms prior to transmitting them through the cable 182. First, asingle transmission line in the cable 182 can transmit multiple digitalwaveforms, each generated by different sensors. This includes multipleECG waveforms (corresponding, e.g., to vectors associated with three,five, and twelve-lead ECG systems) from the ECG circuit mounted in thebulkhead 174, along with waveforms associated with the x, y, and z-axesof accelerometers mounted in the bulkheads 174, 196. More sophisticatedECG circuits (e.g. five and twelve-lead systems) can plug into thewrist-worn transceiver to replace the three-lead system shown in FIGS.27A and 27B.

FIG. 28 shows a close-up view of the wrist-worn transceiver 172. Asdescribed above, it attaches to the patient's wrist using a flexiblestrap 190 which threads through two D-ring openings in a plastic housing206. The transceiver 172 features a touchpanel display 220 that rendersa GUI 173 which is altered depending on the viewer (typically thepatient or a medical professional). Specifically, the transceiver 172includes a small-scale infrared barcode scanner 202 that, during use,can scan a barcode worn on a badge of a medical professional. Thebarcode indicates to the transceiver's software that, for example, anurse or doctor is viewing the user interface. In response, the GUI 173displays vital sign data and other medical diagnostic informationappropriate for medical professionals. Using this GUI 173, the nurse ordoctor, for example, can view the vital sign information, set alarmparameters, and enter information about the patient (e.g. theirdemographic information, medication, or medical condition). The nursecan press a button on the GUI 173 indicating that these operations arecomplete. At this point, the display 220 renders an interface that ismore appropriate to the patient, such as time of day and battery power.

The transceiver 172 features three CAN connectors 204 a-c on the side ofits upper portion, each which supports the CAN protocol and wiringschematics, and relays digitized data to the internal CPU. Digitalsignals that pass through the CAN connectors include a header thatindicates the specific signal (e.g. ECG, ACC, or pressure waveform fromthe cuff-based module) and the sensor from which the signal originated.This allows the CPU to easily interpret signals that arrive through theCAN connectors 204 a-c, such as those described above corresponding toRR, and means that these connectors are not associated with a specificcable. Any cable connecting to the transceiver can be plugged into anyconnector 204 a-c. As shown in FIG. 27A, the first connector 204 areceives the cable 182 that transports a digitized ECG waveformdetermined from the ECG circuit and electrodes, and digitized ACCwaveforms measured by accelerometers in the cable bulkhead 174 and thebulkhead portion 196 associated with the ECG cable 182.

The second CAN connector 204 b shown in FIG. 28 receives the cable 186that connects to the pneumatic cuff-based system 185 used for thepressure-dependent indexing measurement (shown in FIG. 27A). Thisconnector 204 b receives a time-dependent pressure waveform delivered bythe pneumatic system 185 to the patient's arm, along with values forSYS, DIA, and MAP values determined during the indexing measurement. Thecable 186 unplugs from the connector 204 b once the indexing measurementis complete, and is plugged back in after approximately four hours foranother indexing measurement.

The final CAN connector 204 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, ventilator, oret-CO2 delivery system. As described above, digital informationgenerated by these systems will include a header that indicates theirorigin so that the CPU can process them accordingly.

The transceiver includes a speaker 201 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VOIP). For example, using the speaker 201 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in FIG. 28, and use this as a communication device.In this application, the transceiver 172 worn by the patient functionsmuch like a conventional cellular telephone or ‘walkie talkie’: it canbe used for voice communications with the medical professional and canadditionally relay information describing the patient's vital signs andmotion. The speaker can also enunciate pre-programmed messages to thepatient, such as those used to calibrate the chest-worn accelerometersfor a posture calculation, as described above.

Other Embodiments of the Invention

RR can also be calculated using a combination of ACC, ECG, PPG, IP, andother signals using algorithms that differ from those described above.For example, these signals can be processed with an averaging algorithm,such as one using a weighted average, to determine a single waveformthat can then be processed to determine RR. Or the ACC waveform can beused alone, without being integrated in an adaptive filtering algorithm,to determine RR without relying on IP. In this case the ACC waveform isfiltered with a simple bandpass filter, e.g. a finite impulse responsefilter, with a set passband (e.g. 0.01→5 Hz). Similarly, multiple ACCwaveforms, such as those measured along axes (e.g. the x or y-axes)orthogonal to the vector normal to the patient's chest (i.e. thez-axis), can be processed with or without adaptive filtering todetermine RR. In this case the waveforms may be averaged together with aweighted average to generate a single waveform, which is then filtered,derivatized, and signal processed as described above with reference toFIG. 3 to determine RR. Similarly, envelopes associated with the ECG andPPG waveforms can be processed in a similar manner to determine RR. Instill other embodiments, other sensors, such as ultra wide-band radar oracoustic sensors, can detect signals indicative of RR and used with ACCor IP waveforms and the adaptive filtering approach described above todetermine RR. Here, the alternative sensors are typically used toreplace measurement of the IP waveform, although they can also be usedto replace measurement of the ACC waveform. An acoustic sensor suitablefor this application is described, for example, in the followingco-pending patent application, the contents of which are incorporatedherein by reference: DEVICE FOR DETERMINING RESPIRATORY RATE AND OTHERVITAL SIGNS (U.S. Ser. No. 12/171,886; filed Jul. 12, 2008).

In addition to those methods described above, the body-worn monitor canuse a number of additional methods to calculate blood pressure and otherproperties from the optical and electrical waveforms. These aredescribed in the following co-pending patent applications, the contentsof which are incorporated herein by reference: 1) CUFFLESSBLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM(U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FORMEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr. 7, 2004);3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WEB SERVICESINTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004); 4) VITAL SIGNMONITOR FOR ATHLETIC APPLICATIONS (U.S.S.N.; filed Sep. 13, 2004); 5)CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILE DEVICE(U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 6) BLOOD PRESSUREMONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S. Ser. No.10/967,610; filed Oct. 18, 2004); 7) PERSONAL COMPUTER-BASED VITAL SIGNMONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 8) PATCH SENSORFOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No. 10/906,315;filed Feb. 14, 2005); 9) PATCH SENSOR FOR MEASURING VITAL SIGNS (U.S.Ser. No. 11/160,957; filed Jul. 18, 2005); 10) WIRELESS, INTERNET-BASEDSYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OF PATIENTS IN AHOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719; filed Sep. 9,2005); 11) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS (U.S. Ser. No.11/162,742; filed Sep. 21, 2005); 12) CHEST STRAP FOR MEASURING VITALSIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005); 13) SYSTEM FORMEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING A GREEN LIGHTSOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 14) BILATERALDEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S. Ser. No.11/420,281; filed May 25, 2006); 15) SYSTEM FOR MEASURING VITAL SIGNSUSING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652; filed May26, 2006); 16) BLOOD PRESSURE MONITOR (U.S. Ser. No. 11/530,076; filedSep. 8, 2006); 17) TWO-PART PATCH SENSOR FOR MONITORING VITAL SIGNS(U.S. Ser. No. 11/558,538; filed Nov. 10, 2006); and, 18) MONITOR FORMEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES (U.S. Ser. No.11/682,177; filed Mar. 5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other measurement techniques, such as conventional oscillometrymeasured during deflation, can be used to determine SYS for theabove-described algorithms. Additionally, processing units and probesfor measuring pulse oximetry similar to those described above can bemodified and worn on other portions of the patient's body. For example,pulse oximetry probes with finger-ring configurations can be worn onfingers other than the thumb. Or they can be modified to attach to otherconventional sites for measuring SpO2, such as the ear, forehead, andbridge of the nose. In these embodiments the processing unit can be wornin places other than the wrist, such as around the neck (and supported,e.g., by a lanyard) or on the patient's waist (supported, e.g., by aclip that attaches to the patient's belt). In still other embodimentsthe probe and processing unit are integrated into a single unit.

In other embodiments, a set of body-worn monitors can continuouslymonitor a group of patients, wherein each patient in the group wears abody-worn monitor similar to those described herein. Additionally, eachbody-worn monitor can be augmented with a location sensor. The locationsensor includes a wireless component and a location-processing componentthat receives a signal from the wireless component and processes it todetermine a physical location of the patient. A processing component(similar to that described above) determines from the time-dependentwaveforms at least one vital sign, one motion parameter, and an alarmparameter calculated from the combination of this information. Awireless transceiver transmits the vital sign, motion parameter,location of the patient, and alarm parameter through a wireless system.A remote computer system featuring a display and an interface to thewireless system receives the information and displays it on a userinterface for each patient in the group.

In embodiments, the interface rendered on the display at the centralnursing station features a field that displays a map corresponding to anarea with multiple sections. Each section corresponds to the location ofthe patient and includes, e.g., the patient's vital signs, motionparameter, and alarm parameter. For example, the field can display a mapcorresponding to an area of a hospital (e.g. a hospital bay or emergencyroom), with each section corresponding to a specific bed, chair, orgeneral location in the area. Typically the display renders graphicalicons corresponding to the motion and alarm parameters for each patientin the group. In other embodiments, the body-worn monitor includes agraphical display that renders these parameters directly on the patient.

Typically the location sensor and the wireless transceiver operate on acommon wireless system, e.g. a wireless system based on 802.11,802.15.4, or cellular protocols. In this case a location is determinedby processing the wireless signal with one or more algorithms known inthe art. These include, for example, triangulating signals received fromat least three different base stations, or simply estimating a locationbased on signal strength and proximity to a particular base station. Instill other embodiments the location sensor includes a conventionalglobal positioning system (GPS).

The body-worn monitor can include a first voice interface, and theremote computer can include a second voice interface that integrateswith the first voice interface. The location sensor, wirelesstransceiver, and first and second voice interfaces can all operate on acommon wireless system, such as one of the above-described systems basedon 802.11 or cellular protocols. The remote computer, for example, canbe a monitor that is essentially identical to the monitor worn by thepatient, and can be carried or worn by a medical professional. In thiscase the monitor associated with the medical professional features a GUIwherein the user can select to display information (e.g. vital signs,location, and alarms) corresponding to a particular patient. Thismonitor can also include a voice interface so the medical professionalcan communicate directly with the patient.

FIGS. 29A, 29B show yet another alternate embodiment of the inventionwherein a sensor unit 255 attaches to the abdomen of a patient 10 usingan electrode 24 normally attached to the lower left-hand portion of thepatient's torso. Specifically, the sensor unit 255 includes a connector253 featuring an opening that receives the metal snap or rivet presenton most disposable ECG electrodes. Connecting the connector 245 to theelectrode's rivet holds the sensor unit 255 in place. This configurationreduces the number of cables in the body-worn monitor, and additionallysecures an accelerometer 12 to the patient's abdomen. This is typicallythe part of their torso that undergoes the greatest motion duringrespiration, and thus generates ACC waveforms with the highest possiblesignal-to-noise ratio. Also contained within the sensor unit 255 are theECG circuit 26, the IP circuit 27, and a temperature sensor 33.

To measure IP and ECG waveforms, the sensor unit 255 connects throughcables 250 a, 250 b to electrodes 20, 22 attached, respectively, to theupper right-hand and left-hand portions of the patient's torso. Thissystem measures RR using the adaptive filtering approach describedabove, and has the additional advantage of measuring a relatively largeACC signals indicating respiration-induced motions of the patient'sabdomen. As described above, these signals are typically generated bythe z-axis of the accelerometer 12, which is normal to the patient'storso. ACC signals along the x and y-axes can be additionally processedto determine the patient's posture and activity level, as describedabove. Once RR and these motion-related properties are measured, atransceiver in the sensor unit (not shown in the figure) transmits themin the form of a digital data stream through a cable 251 to thewrist-worn transceiver for further processing.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A method for determining a respiration rate valuefrom a patient, comprising the following steps: (a) measuring a firsttime-dependent signal by detecting a modulated electrical currentpassing through the patient's torso; (b) measuring a secondtime-dependent signal by detecting respiration-induced movements in thepatient's torso with at least one motion sensor; (c) determining if thefirst and second time-dependent signals are acceptable for furtherprocessing based on a motion-related event indicative of patient motionthat is not related to the patient's respiration rate value, wherein themotion-related event is determined by processing signals from at leastone motion sensor; and (d) collectively processing both the first andsecond time-dependent signals to determine a value for respiration ratecorresponding to a period when the motion-related event indicates thatthe first and second time-dependent signals are acceptable for furtherprocessing by (i) removing noise components from the firsttime-dependent signal by filtering the first time-dependent signal witha first non-adaptive filter to provide a first filtered signal; (ii)removing noise components from the second time-dependent signal byfiltering the second time-dependent signal with a second non-adaptivefilter to provide a second filtered signal; (iii) filtering the secondfiltered signal with an adaptive filter utilizing filter parametersderived from the first filtered signal to provide a third filteredsignal; and determining the respiration rate value from the thirdfiltered signal.